The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
A sparse matrix is a matrix in which a vast majority of the elements has a value of zero. Sparse matrices are widely used in many practical applications across various industries. For example, text processing generates sparse matrices, and computing document similarity involves sparse matrix multiplication. Additionally, sparse matrix multiplication plays a significant role in computer graphics. Furthermore, many graph problems, such as breadth-first searches and algebraic multigrid methods, involve sparse matrices.
Sparse matrix multiplication can be computationally intensive, especially when it involves matrices with a large number of elements. For the sake of clarity and ease of explanation, however, techniques for efficiently multiplying sparse matrices are described herein with reference to the toy example of FIG. 1.